Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selection

arXiv:2404.06324v1 Announce Type: cross
Abstract: Existing studies on federated learning (FL) are mostly focused on system orchestration for static snapshots of the network and making static control decisions (e.g., spectrum allocation). However, real-world wireless networks are susceptible to temporal variations of wireless channel capacity and users' datasets. In this paper, we incorporate multi-granular system dynamics (MSDs) into FL, including (M1) dynamic wireless channel capacity, captured by a set of discrete-time events, called $\mathscr{D}$-Events, and (M2) dynamic datasets of users. The …

abstract analysis arxiv capacity control cs.ai cs.lg cs.ni decisions dynamic federated learning however mac making network networks orchestration performance performance analysis ran spectrum studies temporal type wireless world